#install.packages("corrplot")
#install.packages("dendextend")
#install.packages("docxtractr")
#install.packages("dplyr")
#install.packages("Formula")
#install.packages("ggfortify")
#install.packages("ggplot2", dependencies = TRUE)
#install.packages("ggpubr")
#install.packages("Hmisc")
#install.packages("lattice")
#install.packages("readxl")
#install.packages("survival")
#install.packages("tidyr")
#install.packages("tidyverse")
library(corrplot)
library(dendextend)
library(docxtractr)
library(dplyr)
library(Formula)
library(ggfortify)
library(ggplot2)
library(ggpubr)
library(Hmisc)
library(lattice)
library(readxl)
library(survival)
library(tidyr)
library(tidyverse)Under abiotic stress such as salinity, several morphological and physiological parameters of plants are affected. As function of their location, plants from the same species can exhibit different tolerance to abiotic stress. In the study we will review, 25 accessions of Eruca sativa from different regions of Pakistan were analyzed. Each accession have been separated into two groups according to the received treatment: first group underwent salt stress (150 mM NaCl) during two weeks and the second was the control. Following that 19 morpho-physiological traits were measured.
The data was obtained from the paper "Evaluation of salt tolerance in Eruca sativa accessions based on morpho-physiological traits" (Afsar et. al 2020)
Our main objectives are:
Main hypothesis:
What have you done? And what did you use to do it? Step by step, so anyone could do it again by reading this part.
First we had to download the data.xls file from the paper. The tables needed to be tidied (column names organized, empty cells filled, the values formatted, etc.). Then we had to download the location of accessions.doc data and tidied this table as well.
In order to be able to work with all of morpho-physiological parameters we had to unify them (because of different number of replicates in different categories of morpho-physiological traits measures). The unification was done trough the mean in every accession and treatment. With unified data we created a summary table (Table 1).
Once we had the summary table, we tested the normality of variables with Shapiro–Wilk test. Our Null Hypothesis (H0) was that morpho-physiological parameters' distribution was normal. The results for every group of morpho-physiological traits were summarized in tables (Table 2-6).
If the H0 was accepted (p > 0.05) we used ANOVA, otherwise (p < 0.05) we proceeded with Kruskal-Wallis test to see if there was a significant difference between salt-treated and control plants for each variable. The parameters' distributions and results of normality tests were visualized in the boxplot figures (Figure 2.1-2.19)
Additionally, we calculated mean and standard deviation values of treated and control plants and summarized them into tables (Table 2.1-2.5).
In order to see the degree of association between the morpho-physiological parameters (and, additionally, elevation) we performed Pearson correlation analysis. The results were visualized in the Figure 3.1??.
We started doing Pearson correlation using membership function values (MFV) of the 11 selected salt tolerance traits, the results were very similar to that in the article. But we discarded that analysis as we didn't understood the utility of it.
Instead we computed Pearson correlation on the means of all salt tolerance traits (treatment and control), as it shows (prior to calculating MFV) which values has positive correlation and which has the negative, thus allowing us to choose the appropriate formula for MFV calculation (see explanation of MFV calculation in the next section).
In order to evaluate degree of salt tolerance (to later define the susceptible and resistant accessions), first we needed to convert all morpho-physiological parameters (control and treatment) for each accession to the salt-tolerance index (STI). STI is the ratio of the values of every morpho-physiological parameters of treated plants to control .
Here we deviated from the paper in the method to calculate STI. We considered that the way the authors calculated STI on accessions was not appropriate. We think that they arbitrary paired plants from treatment and control. Instead, we calculated STI ratio on the means of treated plants and control for every parameter. STI results are summarized in Table 3.1.
Membership function value allows to make the different variables comparable between each other, so they can be used together in order to make a ranking.
We calculated the MVF values using the formula provided in the article: \(Xp=[(X−Xmin)/(Xmax−Xmin)]×100%\). For the traits inversely related to salt tolerance (Electrolyte leakage, Na, K, Ca) we used the second formula: \(Xp=[1−(X−Xmin)/(Xmax−Xmin)]×100%\) To define which morpho-physiological parameters were inversly related, we used the results of the correlation analysis.
??We performed the Principal Component Analysis on the MFV values of all the variables, except for RWC and ICO2 because they did not show significant difference between treatment and control. We used 6 highest ranked variables from the results: PH, SL, RL, NL, DW, FW, LA, K_Na.
excluded: non significant paramterers (RWC, ICO2) Excluded non correlating parameters (pearson): Ca, K, Mg, Na
In order to evaluate salt tolerance, we made accession groups that behave similarly under salt stress. This was based on the MFV mean of the 6 highest ranked physio-morphological parameters from PCA analysis.
The ranking was done by ordering MFV mean values from the highest to the smallest one. Then the groups were adressed according to the same logic as in the paper (with higher MFV mean value corresponding to higher salt tolerance): 4% accessions were classified as highly tolerant, 16% as tolerant, 60% as moderately tolerant, 16% as sensitive and finally 4% as highly sensitive.
The dendogram was established based on MFV mean (6 highest). And 4 groups were formed as in the paper work.
Additionally, we wanted to check if the salt tolerance was correlated to elevation. So we made a Pearson correlation analysis between MFV mean (6 highest) and elevation.
We still need to add map with accession and check for correlation between altitude and MFV ranking.
? overlaping accessions were removed (11 and 15) 11 overlap with 9 and 15 overlap with 12
###----Data download and transformation----###
##Downloading the .xlsx data
#download excel sheet from internet and store it into an Excel object
Excel <- "https://dfzljdn9uc3pi.cloudfront.net/2020/9749/1/Raw_data_Afsar_et_al.%2C_2020-PeerJ_20.5.2020.xlsx"
#create the folder "data" if it doesn't exist
D <- "data"
if (!dir.exists(D)) dir.create(D)
f <- paste0 (D, "/data.xlsx")
#download excel into the folder "data" (created beforehand) under the name "data.xlsx"
download.file(Excel, f, mode="wb")
#get the names of the excel sheets
excel.sheet <- excel_sheets(f)
##Tidying the .xlsx data
#create a list with the 4 sheet table from excel to tidy it further
X <- list()
for (i in excel.sheet){
X[[i]] <- as.data.frame(read_xlsx(f,i))
}
#organize column names and fill the first 2 columns
for(i in names(X)){
names(X[[i]])[1:3] <- c("Number", "Code", "Treat_Contr")
X[[i]] <- X[[i]][-1,]
X[[i]] <- fill(X[[i]], 1:2)
}
#transforming columns' data format
for (i in seq_along(X)){
X[[i]][2:3] <- lapply(X[[i]][2:3], as.factor)
X[[i]][-(2:3)] <- lapply(X[[i]][-(2:3)], as.numeric)
}
#creating data frames from different .xlsx sheets
Morpho_t <- X$`Morphological traits`
Weight_ion <- X$`FW DW RWC Ions EL`
Chloro_c <- X$`Chlorophyll content`
Gas_e <- X$`Gas Exchange parameters`
#changing names of the columns for more convinient use
names(Morpho_t)[4:8] <- c('Shoot_Length',
'Root_Length',
'Plant_Height',
'Number_Leaves',
'Leaf_Area')
names(Chloro_c)[4] <- c('Chlorophyll_Content')
names(Weight_ion) [4:12] <- c('Fresh_Weight',
'Dry_Weight',
'RWC',
'Na',
'K',
'Ca',
'Mg',
'K_Na',
'Electrolyte_Leakage')
names(Gas_e) [4:7] <- c('Photsynthesis_Rate',
'Intercellular_CO2',
'Transpiration_Rate',
'Stomatal_Conductance')
#adding NA cells into the missing 7th and 16th accession
#creating a data frame with the missing data first
Gas_e_miss <- data.frame (Number = rep(c(7, 16), each = 8),
Code = rep(c("Es-7", "Es-16"), each = 8),
Treat_Contr = rep(c("Treatment", "Control",
"Treatment", "Control"), each = 4),
Photsynthesis_Rate = rep(NA, 16),
Intercellular_CO2 = rep(NA, 16),
Transpiration_Rate = rep(NA, 16),
Stomatal_Conductance = rep(NA, 16))
#changing the numbers in "Number" column
Gas_e$Number <- replace(Gas_e$Number, , rep(c(1:6, 8:15, 17:25), each = 8))
#adding NA cells into the missing 7th and 16th accession
if (!(is.element('Es-7', Gas_e$Code))) Gas_e <- rbind(Gas_e, Gas_e_miss)
#sorting the variables according to Number
Gas_e <- Gas_e[order(Gas_e$Number),]
#rewriting row names to correspond to the ordered data
row.names(Gas_e) <- c(1:200)
##Downloading the .docx data
#downloading data for the location of accessions
word <- "https://dfzljdn9uc3pi.cloudfront.net/2020/9749/1/Table_S1.docx"
w <- paste0(D, "/location.docx")
download.file(word, w, mode="wb")
##Tidying the .docx data
#creating data frame for the location of accessions
Acc_loc <- docx_extract_all_tbls(read_docx(w, track_changes = NULL),
guess_header = TRUE, preserve = FALSE,
trim = TRUE)
Acc_loc <- as.data.frame(Acc_loc)
#removing m from Elevation column
Acc_loc$Elevation <- sub(' m', '', Acc_loc$Elevation)
#transforming data in the accession_loc
Acc_loc[c(1,6)] <- lapply(Acc_loc[c(1,6)], as.numeric)
Acc_loc[-c(1,6)] <- lapply(Acc_loc[-c(1,6)], as.factor)
#In order to fit the length of this table to the length of the ultimate table
#we need to double all the rows
Acc_loc_double <- Acc_loc[rep(1:25, each = 2), ]
## Creating the summarized table to work with
#create summary table for morphological trait
a <- Morpho_t %>%
group_by(Number, Code, Treat_Contr) %>%
summarise(Shoot_Length = mean(Shoot_Length),
Root_Length = mean(Root_Length),
Plant_Height = mean(Plant_Height),
Number_Leaves = mean(Number_Leaves),
Leaf_Area = mean(Leaf_Area))
# remove NA values from Weight_ion table
Weight_ion <- na.omit(Weight_ion)
#Create summary table for Weight ion
b <- Weight_ion %>%
group_by(Number, Code, Treat_Contr) %>%
summarise(Fresh_Weight= mean(Fresh_Weight),
Dry_Weight= mean(Dry_Weight),
RWC = mean(RWC),
Na =mean(Na),
K =mean(K),
Ca =mean(Ca),
Mg= mean(Mg),
K_Na =mean(K_Na),
Electrolyte_Leakage= mean(Electrolyte_Leakage))
#take only the columns number 4 to 12 to avoid repetition of number, code and treatment
b <- b[4:12]
#Create summary table for Weight_ion
c <- Chloro_c %>%
group_by(Number, Code, Treat_Contr) %>%
summarise(Chlorophyll_Content = mean(Chlorophyll_Content))
#take only the column number 4 to avoid repetition of number, code and treatment
c <- c[4]
#Create summary table for Gas_e
d <- Gas_e %>%
group_by(Number, Code, Treat_Contr) %>%
summarise(Photsynthesis_Rate= mean(Photsynthesis_Rate),
Intercellular_CO2= mean(Intercellular_CO2),
Transpiration_Rate= mean(Transpiration_Rate),
Stomatal_Conductance=mean(Stomatal_Conductance))
#take only the columns number 4 to 7 to avoid repetition of number, code and treatment
d <- d[4:7]
#add Acc_loc data to the table
e <- Acc_loc_double
e <- e[6]
#create data frame with all the summarized tables
table <- data.frame(a, b, c, d, e)
##Creating the caption for the summary table
knitr::kable(table, caption = "Table 1.1 Mean values of salt tolerance traits for each accession and treatment type", align="c" , digits = round(2))| Number | Code | Treat_Contr | Shoot_Length | Root_Length | Plant_Height | Number_Leaves | Leaf_Area | Fresh_Weight | Dry_Weight | RWC | Na | K | Ca | Mg | K_Na | Electrolyte_Leakage | Chlorophyll_Content | Photsynthesis_Rate | Intercellular_CO2 | Transpiration_Rate | Stomatal_Conductance | Elevation | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | Es-1 | Control | 1.89 | 2.40 | 4.16 | 4.20 | 0.05 | 343.10 | 20.93 | 75.84 | 4.60 | 8.72 | 4.02 | 2.74 | 1.90 | 51.07 | 16.88 | 12.41 | 286.0 | 3.16 | 0.22 | 14 |
| 1.1 | 1 | Es-1 | Treatment | 1.69 | 1.54 | 3.08 | 2.60 | 0.03 | 171.97 | 18.06 | 89.81 | 21.50 | 11.55 | 3.51 | 2.54 | 0.54 | 48.42 | 13.65 | 4.62 | 245.5 | 1.64 | 0.08 | 14 |
| 2 | 2 | Es-2 | Control | 2.83 | 1.93 | 4.99 | 4.40 | 0.08 | 173.13 | 11.97 | 71.60 | 6.04 | 8.81 | 4.28 | 3.49 | 1.46 | 61.86 | 21.17 | 2.74 | 271.5 | 0.90 | 0.04 | 17 |
| 2.1 | 2 | Es-2 | Treatment | 1.54 | 1.27 | 2.53 | 2.80 | 0.04 | 58.84 | 9.00 | 91.74 | 23.86 | 12.99 | 3.74 | 2.83 | 0.55 | 62.49 | 15.20 | 1.78 | 288.5 | 1.12 | 0.04 | 17 |
| 3 | 3 | Es-3 | Control | 3.51 | 2.35 | 4.62 | 4.40 | 0.16 | 321.27 | 15.93 | 76.90 | 7.44 | 9.59 | 5.69 | 3.79 | 1.31 | 64.60 | 18.67 | 17.00 | 290.5 | 4.18 | 0.31 | 579 |
| 3.1 | 3 | Es-3 | Treatment | 0.69 | 0.45 | 1.19 | 1.33 | 0.01 | 32.76 | 3.93 | 89.22 | 17.00 | 14.46 | 5.74 | 3.55 | 0.85 | 78.67 | 13.20 | 1.34 | 258.0 | 0.56 | 0.02 | 579 |
| 4 | 4 | Es-4 | Control | 3.56 | 2.75 | 6.05 | 4.60 | 0.10 | 347.55 | 19.72 | 96.03 | 7.08 | 6.94 | 2.69 | 5.11 | 0.98 | 60.97 | 16.90 | 13.00 | 279.0 | 3.55 | 0.21 | 537 |
| 4.1 | 4 | Es-4 | Treatment | 2.07 | 1.98 | 3.83 | 3.80 | 0.04 | 165.95 | 12.45 | 96.34 | 22.16 | 9.74 | 2.92 | 3.88 | 0.44 | 65.64 | 15.88 | 3.40 | 273.0 | 1.38 | 0.06 | 537 |
| 5 | 5 | Es-5 | Control | 2.48 | 1.65 | 4.51 | 4.40 | 0.07 | 222.80 | 12.93 | 91.39 | 9.97 | 12.06 | 5.29 | 7.24 | 1.21 | 49.65 | 23.72 | 9.11 | 280.0 | 3.45 | 0.20 | 523 |
| 5.1 | 5 | Es-5 | Treatment | 1.08 | 0.97 | 2.09 | 2.00 | 0.06 | 170.20 | 10.83 | 94.64 | 27.53 | 13.02 | 4.01 | 4.89 | 0.47 | 65.76 | 16.82 | 2.62 | 312.5 | 1.94 | 0.05 | 523 |
| 6 | 6 | Es-6 | Control | 2.01 | 1.48 | 3.55 | 4.60 | 0.08 | 753.50 | 32.50 | 98.75 | 10.69 | 12.13 | 3.12 | 5.12 | 1.14 | 59.58 | 17.08 | 4.96 | 281.5 | 3.88 | 0.20 | 186 |
| 6.1 | 6 | Es-6 | Treatment | 1.23 | 1.04 | 2.18 | 3.80 | 0.03 | 235.50 | 10.32 | 95.90 | 25.12 | 14.73 | 2.99 | 3.84 | 0.59 | 58.45 | 12.58 | 1.22 | 315.0 | 1.03 | 0.03 | 186 |
| 7 | 7 | Es-7 | Control | 1.95 | 2.19 | 4.08 | 4.00 | 0.08 | 323.13 | 10.60 | 97.49 | 13.15 | 16.65 | 3.98 | 3.69 | 1.27 | 46.84 | 26.30 | NA | NA | NA | NA | 159 |
| 7.1 | 7 | Es-7 | Treatment | 1.49 | 1.15 | 2.69 | 2.40 | 0.06 | 96.48 | 8.52 | 96.00 | 30.73 | 16.97 | 3.04 | 2.71 | 0.55 | 50.04 | 31.32 | NA | NA | NA | NA | 159 |
| 8 | 8 | Es-8 | Control | 2.97 | 1.80 | 4.66 | 5.00 | 0.07 | 245.13 | 11.20 | 94.58 | 15.59 | 17.60 | 7.78 | 9.49 | 1.13 | 69.75 | 9.80 | 2.86 | 300.5 | 1.57 | 0.07 | 97 |
| 8.1 | 8 | Es-8 | Treatment | 0.92 | 1.05 | 1.98 | 2.40 | 0.04 | 76.17 | 5.59 | 92.02 | 31.90 | 21.97 | 6.39 | 8.85 | 0.69 | 83.50 | 6.93 | 1.54 | 333.0 | 1.30 | 0.05 | 97 |
| 9 | 9 | Es-9 | Control | 2.42 | 2.23 | 4.65 | 4.00 | 0.07 | 60.59 | 3.48 | 91.56 | 16.21 | 33.40 | 8.99 | 8.11 | 2.07 | 50.83 | 27.40 | 14.01 | 255.0 | 3.46 | 0.23 | 355 |
| 9.1 | 9 | Es-9 | Treatment | 1.71 | 1.39 | 3.10 | 3.00 | 0.04 | 50.85 | 2.72 | 90.26 | 31.85 | 41.31 | 13.46 | 5.33 | 1.30 | 56.62 | 31.32 | 2.46 | 251.0 | 0.68 | 0.03 | 355 |
| 10 | 10 | Es-10 | Control | 1.38 | 1.61 | 3.04 | 5.00 | 0.08 | 435.07 | 16.00 | 96.41 | 9.82 | 12.51 | 4.69 | 3.44 | 1.30 | 55.62 | 18.52 | 8.80 | 281.5 | 3.48 | 0.17 | 840 |
| 10.1 | 10 | Es-10 | Treatment | 1.06 | 0.75 | 1.89 | 2.20 | 0.07 | 177.52 | 7.18 | 94.73 | 35.98 | 16.71 | 8.52 | 2.91 | 0.46 | 56.00 | 13.00 | 2.51 | 258.5 | 1.23 | 0.06 | 840 |
| 11 | 11 | Es-11 | Control | 2.06 | 1.89 | 3.99 | 4.40 | 0.06 | 205.72 | 7.28 | 94.66 | 15.82 | 18.40 | 9.79 | 4.41 | 1.17 | 54.23 | 14.32 | 3.29 | 290.0 | 1.65 | 0.07 | 355 |
| 11.1 | 11 | Es-11 | Treatment | 1.27 | 1.13 | 2.33 | 3.60 | 0.05 | 144.61 | 6.43 | 95.90 | 18.54 | 20.71 | 11.00 | 4.25 | 1.12 | 54.21 | 9.35 | 1.16 | 329.0 | 0.63 | 0.03 | 355 |
| 12 | 12 | Es-12 | Control | 1.78 | 1.66 | 3.44 | 4.40 | 0.06 | 247.10 | 10.80 | 94.75 | 16.64 | 14.60 | 4.02 | 6.07 | 0.88 | 57.91 | 15.67 | 9.07 | 255.5 | 3.14 | 0.15 | 178 |
| 12.1 | 12 | Es-12 | Treatment | 1.37 | 1.22 | 2.36 | 3.00 | 0.02 | 193.27 | 9.27 | 95.20 | 20.31 | 11.98 | 7.14 | 5.51 | 0.59 | 59.02 | 14.37 | 3.31 | 281.5 | 1.84 | 0.06 | 178 |
| 13 | 13 | Es-13 | Control | 2.73 | 2.50 | 5.34 | 5.00 | 0.07 | 449.30 | 10.90 | 96.10 | 10.56 | 15.25 | 5.45 | 4.46 | 1.44 | 47.15 | 15.00 | 14.80 | 294.0 | 3.64 | 0.20 | 140 |
| 13.1 | 13 | Es-13 | Treatment | 1.97 | 1.44 | 3.41 | 4.00 | 0.04 | 176.71 | 9.17 | 94.59 | 27.91 | 15.77 | 8.00 | 3.15 | 0.57 | 47.84 | 9.08 | 2.50 | 223.5 | 0.68 | 0.03 | 140 |
| 14 | 14 | Es-14 | Control | 2.29 | 2.41 | 4.79 | 5.60 | 0.08 | 426.00 | 14.00 | 97.35 | 12.85 | 13.03 | 4.43 | 4.19 | 1.02 | 49.72 | 24.88 | 8.83 | 294.5 | 2.58 | 0.20 | 190 |
| 14.1 | 14 | Es-14 | Treatment | 1.23 | 1.49 | 2.80 | 3.20 | 0.03 | 165.56 | 8.61 | 94.23 | 35.57 | 18.91 | 10.12 | 3.73 | 0.53 | 55.17 | 17.52 | 3.18 | 274.5 | 1.25 | 0.05 | 190 |
| 15 | 15 | Es-15 | Control | 2.07 | 2.42 | 4.38 | 4.75 | 0.05 | 250.90 | 11.80 | 95.26 | 15.84 | 12.65 | 4.25 | 4.53 | 0.80 | 59.46 | 16.55 | 5.16 | 315.5 | 1.93 | 0.10 | 169 |
| 15.1 | 15 | Es-15 | Treatment | 0.35 | 0.97 | 1.64 | 1.60 | 0.01 | 36.60 | 3.60 | 91.67 | 21.56 | 16.92 | 5.76 | 3.12 | 0.78 | 62.24 | 9.63 | 1.37 | 215.5 | 0.26 | 0.01 | 169 |
| 16 | 16 | Es-16 | Control | 2.42 | 2.31 | 4.65 | 4.60 | 0.07 | 77.00 | 3.67 | 90.82 | 10.68 | 13.63 | 5.49 | 3.87 | 1.30 | 49.17 | 21.22 | NA | NA | NA | NA | 268 |
| 16.1 | 16 | Es-16 | Treatment | 0.69 | 0.45 | 1.23 | 1.33 | 0.01 | 33.03 | 3.01 | 88.90 | 22.88 | 19.37 | 12.14 | 2.83 | 0.84 | 52.06 | 12.67 | NA | NA | NA | NA | 268 |
| 17 | 17 | Es-17 | Control | 2.44 | 2.23 | 4.69 | 5.00 | 0.08 | 210.20 | 10.40 | 94.25 | 17.88 | 17.13 | 7.26 | 6.26 | 0.96 | 60.17 | 15.18 | 1.14 | 340.0 | 1.06 | 0.05 | 207 |
| 17.1 | 17 | Es-17 | Treatment | 0.60 | 0.45 | 1.23 | 1.40 | 0.02 | 36.28 | 3.00 | 90.90 | 47.06 | 26.70 | 22.98 | 4.59 | 0.58 | 63.78 | 9.22 | 0.76 | 315.0 | 0.37 | 0.03 | 207 |
| 18 | 18 | Es-18 | Control | 3.05 | 2.90 | 5.87 | 5.20 | 0.07 | 307.93 | 8.67 | 96.56 | 24.72 | 22.81 | 4.89 | 5.05 | 0.93 | 62.19 | 9.62 | 2.84 | 306.0 | 1.40 | 0.07 | 162 |
| 18.1 | 18 | Es-18 | Treatment | 0.60 | 0.46 | 1.16 | 2.00 | 0.01 | 41.04 | 2.80 | 88.61 | 38.07 | 25.59 | 13.62 | 3.84 | 0.68 | 68.11 | 12.50 | 1.43 | 349.5 | 1.00 | 0.03 | 162 |
| 19 | 19 | Es-19 | Control | 2.25 | 2.18 | 4.43 | 3.40 | 0.04 | 104.92 | 5.34 | 92.96 | 12.51 | 18.37 | 2.61 | 5.99 | 1.47 | 57.77 | 18.75 | 2.16 | 287.0 | 3.64 | 0.20 | 186 |
| 19.1 | 19 | Es-19 | Treatment | 1.23 | 1.49 | 2.80 | 3.20 | 0.03 | 70.57 | 4.08 | 94.69 | 32.73 | 22.20 | 6.84 | 3.78 | 0.68 | 59.43 | 16.52 | 1.62 | 293.0 | 0.56 | 0.04 | 186 |
| 20 | 20 | Es-20 | Control | 1.99 | 2.34 | 4.33 | 3.00 | 0.04 | 42.25 | 3.10 | 88.38 | 13.68 | 26.53 | 13.48 | 6.81 | 1.94 | 62.26 | 22.22 | 12.70 | 238.0 | 3.00 | 0.16 | 186 |
| 20.1 | 20 | Es-20 | Treatment | 0.35 | 0.97 | 2.80 | 1.60 | 0.01 | 30.15 | 2.60 | 91.38 | 48.28 | 33.45 | 21.62 | 4.74 | 0.72 | 65.09 | 14.67 | 1.75 | 215.5 | 0.46 | 0.02 | 186 |
| 21 | 21 | Es-21 | Control | 2.03 | 2.68 | 4.72 | 3.20 | 0.04 | 90.56 | 4.18 | 94.99 | 24.90 | 24.81 | 8.28 | 10.54 | 1.00 | 46.49 | 9.95 | 12.18 | 274.0 | 4.14 | 0.25 | 171 |
| 21.1 | 21 | Es-21 | Treatment | 0.60 | 0.46 | 1.78 | 2.00 | 0.02 | 61.55 | 3.25 | 95.42 | 44.92 | 30.38 | 12.05 | 5.82 | 0.68 | 48.78 | 16.90 | 2.58 | 265.5 | 1.23 | 0.06 | 171 |
| 22 | 22 | Es-22 | Control | 1.99 | 2.47 | 4.33 | 3.40 | 0.04 | 48.30 | 2.02 | 96.41 | 16.72 | 12.71 | 9.38 | 5.53 | 0.76 | 59.96 | 18.98 | 4.26 | 259.0 | 1.32 | 0.06 | 170 |
| 22.1 | 22 | Es-22 | Treatment | 1.22 | 0.78 | 2.13 | 2.00 | 0.04 | 35.92 | 1.32 | 95.93 | 28.21 | 15.63 | 13.33 | 4.66 | 0.56 | 71.26 | 8.02 | 1.24 | 275.0 | 0.82 | 0.03 | 170 |
| 23 | 23 | Es-23 | Control | 1.73 | 1.80 | 3.53 | 3.00 | 0.05 | 138.11 | 4.17 | 94.44 | 17.41 | 10.94 | 8.64 | 3.17 | 0.63 | 63.71 | 21.50 | 5.04 | 246.0 | 1.56 | 0.06 | 210 |
| 23.1 | 23 | Es-23 | Treatment | 0.63 | 0.53 | 1.17 | 2.00 | 0.03 | 37.66 | 3.38 | 90.88 | 26.89 | 16.58 | 9.95 | 2.30 | 0.62 | 66.40 | 8.83 | 0.78 | 264.0 | 0.66 | 0.03 | 210 |
| 24 | 24 | Es-24 | Control | 1.40 | 1.41 | 2.84 | 3.00 | 0.04 | 107.80 | 7.67 | 90.98 | 13.83 | 13.91 | 6.39 | 14.25 | 1.01 | 61.26 | 14.22 | 4.47 | 276.5 | 1.52 | 0.06 | 209 |
| 24.1 | 24 | Es-24 | Treatment | 0.75 | 0.49 | 1.26 | 1.60 | 0.03 | 66.41 | 3.15 | 89.62 | 28.33 | 19.10 | 7.40 | 5.60 | 0.68 | 67.51 | 9.42 | 0.90 | 361.0 | 0.61 | 0.03 | 209 |
| 25 | 25 | Es-25 | Control | 2.39 | 2.74 | 5.18 | 3.00 | 0.04 | 95.34 | 5.29 | 93.99 | 15.45 | 13.94 | 5.81 | 11.36 | 0.91 | 50.58 | 19.83 | 2.27 | 266.0 | 0.99 | 0.06 | 151 |
| 25.1 | 25 | Es-25 | Treatment | 1.17 | 0.64 | 1.84 | 2.00 | 0.04 | 39.93 | 4.31 | 90.57 | 41.97 | 26.23 | 11.76 | 9.95 | 0.62 | 48.61 | 12.95 | 0.71 | 350.5 | 0.38 | 0.03 | 151 |
There is a statistical difference between control and treatment for all morphological traits
Shoot length (SL): Shoot length is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for SL in salt-stressed plants is 1.10 ± 0.48 and 2.30 ± 0.56 for control plants. SL stress index of accessions varied from 16.73 to 89.12
Root length (RL): Root length is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for RL in salt-stressed plants is 0.98 ± 0.43 and 2.17 ± 0.42 for control plants. RL stress index of accessions varied from 15.91 to 73.53
Plant height (PH): Plant height is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for PH in salt-stressed plants is 2.18 ± 0.76 and 4.43 ± 0.77 for control plants. PH stress index of accessions varied from 19.83 to 74.07
Number of leaves(NL): Number of leaves is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for NL in salt-stressed plants is 2.43 ± 0.82 and 4.22 ± 0.78 for control plants. NL stress index of accessions varied from 28.00 to 94.12
Leaf area (LA): Leaf area is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for LA in salt-stressed plants is 0.03 ± 0.02 and 0.07 ± 0.03 for control plants. LA stress index of accessions varied from 75.95 to 100
#Performing Shapiro–Wilk test of normality
spm <- numeric()
nor_spm <- c()
for(i in names(Morpho_t[4:8])) {
an1 <- aov(Morpho_t[, i] ~ Morpho_t$Treat_Contr)
spm[i] <- shapiro.test(an1$residuals)$p.value
nor_spm <- c(nor_spm, ifelse (spm[i] <= 0.05, "False", "True"))
}
#Creating summary table with the results
spm_table <- data.frame ("P-value" = spm, "Normality" = nor_spm)
#Creating the caption for the summary table
knitr::kable(spm_table, caption = "Table 2.1 Shapiro–Wilk test results for morphological traits", align="c")| P.value | Normality | |
|---|---|---|
| Shoot_Length | 0.0000307 | False |
| Root_Length | 0.0187623 | False |
| Plant_Height | 0.0315918 | False |
| Number_Leaves | 0.0000000 | False |
| Leaf_Area | 0.0000000 | False |
#Performing significance test and plotting the results and values distribution
for (i in (4:8)) {
bmorpho <- ggboxplot(Morpho_t,
x = "Treat_Contr",
y = names(Morpho_t[i]),
color = names(Morpho_t[3]),
palette = c("blue", "red"),
add = "jitter") +
stat_compare_means(data = Morpho_t, method = "kruskal.test",
label.x.npc = "center",
label.y.npc = "top") +
theme(legend.position = "None") +
labs(x = "",
caption = paste("Figure 2.", (i-3),
"Distribution boxplot of the mean values of the",
names(Morpho_t[i])))
print(bmorpho)
}Fresh weight (FW): Fresh weight is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for FW in salt-stressed plants 96.22 ± 66.00 and 241.06 ± 164.75 for control plants. FW stress index of accessions varied from 10.20 to 83.93
Dry weight (DW): Dry weight is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for DW in salt-stressed plants is 6.26 ± 4.00 and 10.58 ± 6.89 for control plants. DW stress index of accessions varied from 24.67 to 88.26
#Performing Shapiro–Wilk test of normality
spb <- numeric()
nor_spb <- c()
for(i in names(Weight_ion[4:5])) {
an2 <- aov(Weight_ion[, i] ~ Weight_ion$Treat_Contr)
spb[i] <- shapiro.test(an2$residuals)$p.value
nor_spb <- c(nor_spb, ifelse (spb[i] <= 0.05, "False", "True"))
}
#Creating summary table with the results
spb_table <- data.frame ("P-value" = spb, "Normality" = nor_spb)
#Creating the caption for the summary table
knitr::kable(spb_table, caption = "Table 2.2 Shapiro–Wilk test results for biomass traits", align="c")| P.value | Normality | |
|---|---|---|
| Fresh_Weight | 3e-07 | False |
| Dry_Weight | 7e-07 | False |
#Performing significance test and plotting the results and values distribution
for (i in (4:5)) {
bwei <- ggboxplot(Weight_ion,
x = "Treat_Contr",
y = names(Weight_ion[i]),
color = names(Weight_ion[3]),
palette = c("blue", "red"),
add = "jitter") +
stat_compare_means(data = Weight_ion, method = "kruskal.test",
label.x.npc = "center",
label.y.npc = "top") +
theme(legend.position = "None") +
labs(x = "",
caption = paste("Figure 2.", (i+2),
"Distribution boxplot of the mean values of the",
names(Weight_ion[i])))
print(bwei)
} Relative water content (RWC): Relative water content is not significantly different in the salt-stressed plants compared with the control (p > 0.05). The mean value for RWC in salt-stressed plants 92.77 X ± 2.65 and 92.10 ± 7.00 for control plants.
Electrolyte leakage (EL): Electrolyte leakage is significantly increased in the salt-stressed plants compared with the control (p < 0.05). The mean value for EL in salt-stressed plants is 60.60 ± 9.32 and 56.51 ± 6.48 for control plants.
Chlorophyll content (SPAD or CC): Chlorophyll content is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for CC in salt-stressed plants is 14.06 ± 6.02 and 18.17 ± 4.79 for control plants.
#Performing Shapiro–Wilk test of normality on the RWC and EL
spe <- numeric()
nor_spe <- c()
for(i in names(Weight_ion[c(6,12)])) {
an3 <- aov(Weight_ion[, i] ~ Weight_ion$Treat_Contr)
spe[i] <- shapiro.test(an3$residuals)$p.value
nor_spe <- c(nor_spe, ifelse (spe[i] <= 0.05, "False", "True"))
}
#Creating summary table with the results for RWC and EL
spe_table <- data.frame ("P-value" = spe, "Normality" = nor_spe)
#Performing Shapiro–Wilk test of normality on the SPAD
nor_spc <- c()
an4 <- aov(Chloro_c$Chlorophyll_Content ~ Chloro_c$Treat_Contr)
spc <- shapiro.test(an4$residuals)$p.value
#Creating summary table with the results for SPAD
nor_spc <- c(nor_spc, ifelse (spc <= 0.05, "False", "True"))
spc_table <- data.frame ("P-value" = spc, "Normality" = nor_spc)
row.names(spc_table) <- "Chlorophyll_Content"
#Uniting the summary tables with the results for RWC+EL and SPAD
spec_table <- dplyr::bind_rows(spe_table, spc_table)
#Creating the caption for the summary table
knitr::kable(spec_table, caption = "Table 2.3 Shapiro–Wilk test results for RWC, EL and SPAD", align="c")| P.value | Normality | |
|---|---|---|
| RWC | 0.0000000 | False |
| Electrolyte_Leakage | 0.0009774 | False |
| Chlorophyll_Content | 0.0000000 | False |
#for RWC and EL
for (i in (c(6,12))) {
bwei2 <- ggboxplot(Weight_ion,
x = "Treat_Contr",
y = names(Weight_ion[i]),
color = names(Weight_ion[3]),
palette = c("blue", "red"),
add = "jitter") +
stat_compare_means(data = Weight_ion, method = "kruskal.test",
label.x.npc = "center",
label.y.npc = "top") +
theme(legend.position = "None") +
labs(x = "",
caption = paste("Figure 2.", ifelse((i<7), paste(i+2), paste(i-3)),
"Distribution boxplot of the mean values of the",
names(Weight_ion[i])))
print(bwei2)
} #for SPAD
bchlo <- ggboxplot(Chloro_c,
x = "Treat_Contr",
y = "Chlorophyll_Content",
color = names(Chloro_c[3]),
palette = c("blue", "red"),
add = "jitter")
bchlo + stat_compare_means(data = Chloro_c, method = "kruskal.test",
label.x.npc = "center",
label.y.npc = "top") +
theme(legend.position = "None") +
labs(x = "", caption = paste("Fifure 2. 10 Distribution boxplot of the mean values of the",
names(Chloro_c[4])))Sodium (Na): Sodium is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for Na in salt-stressed plants is 30.03 ± 8.84 and 13.60 ± 5.00 for control plants.
Potassium (K): Potassium is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for K in salt-stressed plants is 19.72 ± 7.48 and 15.48 ± 6.09 for control plants.
Calcium (Ca): Calcium is significantly increased in the salt-stressed plants compared with the control (p < 0.05). The mean value for Ca in salt-stressed plants is 9.12 ± 5.32 and 6.03 ± 2.60 for control plants.
Magnesium (Mg): Magnesium is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for Mg in salt-stressed plants is 4.37 ± 1.83 and 5.95 ± 2.85 for control plants.
K/Na ratio: K/Na ratio is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for K/Na ratio in salt-stressed plants is 0.67 ± 0.20 and 1.20 ± 0.36 for control plants.
#Performing Shapiro–Wilk test of normality
spw <- numeric()
nor_spw <- c()
for(i in names(Weight_ion[7:11])) {
an5 <- aov(Weight_ion[, i] ~ Weight_ion$Treat_Contr)
spw[i] <- shapiro.test(an5$residuals)$p.value
nor_spw <- c(nor_spw, ifelse (spw[i] <= 0.05, "False", "True"))
}
#Creating summary table with the results
spw_table <- data.frame ("P-value" = spw, "Normality" = nor_spw)
#Creating the caption for the summary table
knitr::kable(spw_table, caption = "Table 2.4 Shapiro–Wilk test results for mineral ion content ", align="c")| P.value | Normality | |
|---|---|---|
| Na | 0.0002216 | False |
| K | 0.0000000 | False |
| Ca | 0.0000007 | False |
| Mg | 0.0000000 | False |
| K_Na | 0.0000002 | False |
#Performing significance test and plotting the results and values distribution
for (i in (7:11)) {
bwei3 <- ggboxplot(Weight_ion,
x = "Treat_Contr",
y = names(Weight_ion[i]),
color = names(Weight_ion[3]),
palette = c("blue", "red"),
add = "jitter") +
stat_compare_means(data = Weight_ion, method = "kruskal.test",
label.x.npc = "center",
label.y.npc = "top") +
theme(legend.position = "None") +
labs(x = "",
caption = paste("Figure 2.", (i+4),
"Distribution boxplot of the mean values of the",
names(Weight_ion[i])))
print(bwei3)
}Photosynthesis rate (PR): Photosynthesus rate is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for PR in salt-stressed plants is 1.95 ± 1.02 and 7.53 ± 4.84 for control plants.
Intercellular CO2 (ICO2): Intercellular CO2 is not significantly different in the salt-stressed plants compared with the control (p < 0.05). The mean value for ICO2 in salt-stressed plants is 284.70 ± 42.57 and 281.20 ± 22.90 for control plants.
Transpiration rate (TR): Transpiration rate is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for TR in salt-stressed plants is 0.94 ± 0.48 and 2.57 ± 1.13 for control plants.
Stomatal conductance (SC): Stomatal conductance is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for SC in salt-stressed plants is 0.039 ± 0.02 and 0.15 ± 0.08 for control plants.
#Performing Shapiro–Wilk test of normality
spg <- numeric()
nor_spg <- c()
for (i in names(Gas_e[c(4:7)])) {
an6 <- aov(Gas_e[, i] ~ Gas_e$Treat_Contr)
spg[i] <- shapiro.test(an6$residuals)$p.value
nor_spg <- c(nor_spg, ifelse (spg[i] <= 0.05, "False", "True"))
}
#Creating summary table with the results
spg_table <- data.frame ("P-value" = spg, "Normality" = nor_spg)
#Creating the caption for the summary table
knitr::kable(spg_table, caption = "Table 2.5 Shapiro–Wilk test results for gas exchange attributes", align="c")| P.value | Normality | |
|---|---|---|
| Photsynthesis_Rate | 0.0000139 | False |
| Intercellular_CO2 | 0.8962970 | True |
| Transpiration_Rate | 0.0384559 | False |
| Stomatal_Conductance | 0.0000015 | False |
#Performing significance test and plotting the results and values distribution
#for intercellular CO2 (with ANOVA results, as this is the only normally distributed value)
Gas_e_withoutNA <- na.omit(Gas_e)
banova <- ggboxplot(Gas_e_withoutNA,
x="Treat_Contr",
y= "Intercellular_CO2",
color = names(Gas_e_withoutNA[3]),
palette = c("blue", "red"),
add = "jitter")
banova + stat_compare_means(method = "anova") +
theme(legend.position = "None") +
labs(x = "",
caption = paste("Figure 2. 16 Distribution boxplot of the mean values of the",
names(Gas_e_withoutNA[5])))#for photosynthesis rate, transpiration rate, stomatal conductance
#(with Kruskal-Wallis results)
for (i in c(4,6:7)) {
bgas <- ggboxplot(Gas_e_withoutNA,
x = "Treat_Contr",
y = names(Gas_e_withoutNA[i]),
color = names(Gas_e_withoutNA[3]),
palette = c("blue", "red"),
add = "jitter") +
stat_compare_means(data = Gas_e_withoutNA, method = "kruskal.test",
label.x.npc = "center",
label.y.npc = "top") +
theme(legend.position = "None") +
labs(x = "",
caption = paste("Figure 2.", ifelse((i<5), paste(i+13), paste(i+12)),
"Distribution boxplot of the mean values of the",
names(Gas_e_withoutNA[i])))
print(bgas)
}x <- data.frame(dplyr::filter(table, table$Treat_Contr=="Treatment"))
x <- x[-(1:3)]
y <- data.frame(dplyr::filter(table, table$Treat_Contr=="Control"))
y <- y[-(1:3)]
Mean_Treat <- x %>%
summarise(Mean_SL = mean(Shoot_Length),
Mean_SL = mean(Root_Length),
Mean_PH = mean(Plant_Height),
Mean_NL = mean(Number_Leaves),
Mean_LA = mean(Leaf_Area),
Mean_FW = mean(Fresh_Weight),
Mean_DW = mean(Dry_Weight),
Mean_RWC = mean(RWC),
Mean_Na = mean(Na),
Mean_K = mean(K),
Mean_Ca = mean(Ca),
Mean_Mg = mean(Mg),
Mean_K_Na = mean(K_Na),
Mean_EL = mean(Electrolyte_Leakage),
Mean_CC = mean(Chlorophyll_Content),
Mean_PR = mean (Photsynthesis_Rate, na.rm=T),
Mean_ICO2 = mean(Intercellular_CO2, na.rm=T),
Mean_TR = mean(Transpiration_Rate, na.rm=T),
Mean_S = mean(Stomatal_Conductance, na.rm=T))
Mean_Control <- y %>%
summarise(Mean_SL = mean(Shoot_Length),
Mean_SL = mean(Root_Length),
Mean_PH = mean(Plant_Height),
Mean_NL = mean(Number_Leaves),
Mean_LA = mean(Leaf_Area),
Mean_FW = mean(Fresh_Weight),
Mean_DW = mean(Dry_Weight),
Mean_RWC = mean(RWC),
Mean_Na = mean(Na),
Mean_K = mean(K),
Mean_Ca = mean(Ca),
Mean_Mg = mean(Mg),
Mean_K_Na = mean(K_Na),
Mean_EL = mean(Electrolyte_Leakage),
Mean_CC = mean(Chlorophyll_Content),
Mean_PR = mean (Photsynthesis_Rate, na.rm=T),
Mean_ICO2 = mean(Intercellular_CO2, na.rm=T),
Mean_TR = mean(Transpiration_Rate, na.rm=T),
Mean_S = mean(Stomatal_Conductance, na.rm=T))
Treatment <- c('Treatment','Control')
Means <- rbind(Mean_Treat, Mean_Control)
Means$Treat_Contr <- Treatment
Sd_Treatment <- x %>%
summarise(SD_SL = sd(Shoot_Length),
SD_RL = sd(Root_Length),
SD_PH = sd(Plant_Height),
SD_NL = sd(Number_Leaves),
SD_LA = sd(Leaf_Area),
SD_FW = sd(Fresh_Weight),
SD_DW = sd(Dry_Weight),
SD_RWC = sd(RWC),
SD_Na = sd(Na),
SD_K = sd(K),
SD_Ca = sd(Ca),
SD_Mg = sd(Mg),
SD_K_NA = sd(K_Na),
SD_EL = sd(Electrolyte_Leakage),
SD_CC = sd(Chlorophyll_Content),
SD_PR = sd (Photsynthesis_Rate, na.rm=T),
SD_I_CO2 = sd(Intercellular_CO2, na.rm=T),
SD_TR = sd(Transpiration_Rate, na.rm=T),
SD_SC = sd(Stomatal_Conductance, na.rm=T))
Sd_Control <- y %>%
summarise(SD_SL = sd(Shoot_Length),
SD_RL = sd(Root_Length),
SD_PH = sd(Plant_Height),
SD_NL = sd(Number_Leaves),
SD_LA = sd(Leaf_Area),
SD_FW = sd(Fresh_Weight),
SD_DW = sd(Dry_Weight),
SD_RWC = sd(RWC),
SD_Na = sd(Na),
SD_K = sd(K),
SD_Ca = sd(Ca),
SD_Mg = sd(Mg),
SD_K_NA = sd(K_Na),
SD_EL = sd(Electrolyte_Leakage),
SD_CC = sd(Chlorophyll_Content),
SD_PR = sd (Photsynthesis_Rate, na.rm=T),
SD_I_CO2 = sd(Intercellular_CO2, na.rm=T),
SD_TR = sd(Transpiration_Rate, na.rm=T),
SD_SC = sd(Stomatal_Conductance, na.rm=T))
Treatment <- c('Treatment','Control')
SD <- rbind(Sd_Treatment,Sd_Control)
SD$Treat_Contr <- Treatment
knitr::kable(Means, caption = "Table 2.1 Summary table for the mean of treated and control plants")| Mean_SL | Mean_PH | Mean_NL | Mean_LA | Mean_FW | Mean_DW | Mean_RWC | Mean_Na | Mean_K | Mean_Ca | Mean_Mg | Mean_K_Na | Mean_EL | Mean_CC | Mean_PR | Mean_ICO2 | Mean_TR | Mean_S | Treat_Contr |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.98288 | 2.17992 | 2.43456 | 0.03304 | 96.22133 | 6.262533 | 92.76587 | 30.03500 | 19.71913 | 9.120667 | 4.368333 | 0.6673333 | 60.60373 | 14.06147 | 1.947391 | 284.6957 | 0.9404348 | 0.0386957 | Treatment |
| 2.17392 | 4.43336 | 4.22200 | 0.06664 | 241.06840 | 10.582267 | 92.09667 | 13.60367 | 15.48453 | 6.027733 | 5.948000 | 1.1988667 | 56.51267 | 18.17267 | 7.526739 | 281.1957 | 2.5736957 | 0.1458696 | Control |
knitr::kable(SD, caption = "Table 2.2 Summary table for the standard deviation (SD) of treated and control plants")| SD_SL | SD_RL | SD_PH | SD_NL | SD_LA | SD_FW | SD_DW | SD_RWC | SD_Na | SD_K | SD_Ca | SD_Mg | SD_K_NA | SD_EL | SD_CC | SD_PR | SD_I_CO2 | SD_TR | SD_SC | Treat_Contr |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4802925 | 0.4291682 | 0.7607550 | 0.823898 | 0.0156644 | 66.00101 | 3.997402 | 2.650396 | 8.841386 | 7.478913 | 5.315845 | 1.827305 | 0.1959651 | 9.321449 | 6.022014 | 1.017193 | 42.57340 | 0.475834 | 0.0179150 | Treatment |
| 0.5639478 | 0.4196222 | 0.7721049 | 0.780235 | 0.0254147 | 164.75151 | 6.887152 | 6.997688 | 5.007557 | 6.091253 | 2.606707 | 2.854427 | 0.3648619 | 6.479804 | 4.789543 | 4.839646 | 22.89638 | 1.131070 | 0.0801618 | Control |
# table with all the variables but without 3 first columns
all_data <- na.omit(table[4:23])
res1 <- cor.mtest(all_data, conf.level = .95)
M1 <-cor(all_data)
corrplot(M1, p.mat = res1$p, sig.level = .05, type = "lower")Figure 3.1. Pearson correlation table for all variables. X represent non-significative correlation (p-value > 0.05)
We can see that morphological traits are positively and strongly correlated (close to 1). Biomass is also positively correlated to morphological traits.
x <- data.frame(dplyr::filter(table, table$Treat_Contr=="Treatment"))
x <- x[-(1:3)]
y <- data.frame(dplyr::filter(table, table$Treat_Contr=="Control"))
y <- y[-(1:3)]
#salt tolerance index for morphological traits
STI_Shoot_Length <- x[1]/y[1]
STI_Root_Length <- x[2]/y[2]
STI_Plant_Height <- x[3]/y[3]
STI_Number_Leaves <- x[4]/y[4]
STI_Leaf_Area <- x[5]/y[5]
#trying to make a for loop
#STI1 <- data.frame()
#vecteur <- c()
#for (i in names(x)){
# for (j in 1:4){
# name <- paste("STI", i ,sep = "_" )
# name <- x[j]/x[j]
# vecteur <- c(vecteur, name)}}
#salt tolerance index for biomass
STI_Fresh_Weight <- x[6]/y[6]
STI_Dry_Weight <- x[7]/y[7]
#salt tolerance index for RWC, EL and Chlorophyll content
STI_Relative_water_content <- x[8]/y[8]
names(STI_Relative_water_content)[1] <- "Relative_water_content" # change the name of the column of the data
STI_Electrolyte_Leakage <- x[14]/y[14]
STI_Chlorophyll_Content <- x[15]/y[15]
# salt tolerance index for Mineral ion content
STI_Na <- x[9]/y[9]
STI_K <- x[10]/y[10]
STI_Ca <- x[11]/y[11]
STI_Mg <- x[12]/y[12]
STI_K_Na <- x[13]/y[13]
# salt tolerance index for Gas exchange attributes
STI_Photosynthesis_rate <- x[16]/y[16]
STI_Intercellular_CO2 <- x[17]/y[17]
STI_Transpiration_Rate <- x[18]/y[18]
STI_Stomatal_Conductance <- x[19]/y[19]
STI <- data.frame(STI_Shoot_Length,
STI_Root_Length,
STI_Plant_Height,
STI_Number_Leaves,
STI_Leaf_Area,
STI_Fresh_Weight,
STI_Dry_Weight,
STI_Relative_water_content,
STI_Electrolyte_Leakage,
STI_Na,
STI_K,
STI_Ca,
STI_Mg,
STI_K_Na,
STI_Chlorophyll_Content,
STI_Photosynthesis_rate,
STI_Intercellular_CO2,
STI_Transpiration_Rate,
STI_Stomatal_Conductance)
knitr::kable(STI, caption = "Table 4.1. Summary table for salt tolerance index")| Shoot_Length | Root_Length | Plant_Height | Number_Leaves | Leaf_Area | Fresh_Weight | Dry_Weight | Relative_water_content | Electrolyte_Leakage | Na | K | Ca | Mg | K_Na | Chlorophyll_Content | Photsynthesis_Rate | Intercellular_CO2 | Transpiration_Rate | Stomatal_Conductance | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.1 | 0.8912355 | 0.6397671 | 0.7407407 | 0.6190476 | 0.6956522 | 0.5012338 | 0.8625796 | 1.1842036 | 0.9481757 | 4.673913 | 1.3245413 | 0.8746888 | 0.9281364 | 0.2824561 | 0.8084896 | 0.3725332 | 0.8583916 | 0.5174051 | 0.3563218 |
| 2.1 | 0.5455830 | 0.6594203 | 0.5068109 | 0.6363636 | 0.5000000 | 0.3398344 | 0.7523677 | 1.2812980 | 1.0102382 | 3.953065 | 1.4744608 | 0.8739300 | 0.8126195 | 0.3735763 | 0.7181102 | 0.6496350 | 1.0626151 | 1.2375691 | 1.0000000 |
| 3.1 | 0.1974957 | 0.1920136 | 0.2569204 | 0.3027273 | 0.0759494 | 0.1019714 | 0.2466527 | 1.1603017 | 1.2178019 | 2.284498 | 1.5078206 | 1.0076112 | 0.9375000 | 0.6530612 | 0.7071429 | 0.0788235 | 0.8881239 | 0.1327751 | 0.0720000 |
| 4.1 | 0.5805727 | 0.7180233 | 0.6326396 | 0.8260870 | 0.4375000 | 0.4774903 | 0.6312320 | 1.0031933 | 1.0765362 | 3.128941 | 1.4041326 | 1.0880893 | 0.7604439 | 0.4489796 | 0.9398422 | 0.2615385 | 0.9784946 | 0.3887324 | 0.2941176 |
| 5.1 | 0.4346774 | 0.5895884 | 0.4631766 | 0.4545455 | 0.8484848 | 0.7639138 | 0.8376289 | 1.0355254 | 1.3243824 | 2.762542 | 1.0798784 | 0.7580340 | 0.6751035 | 0.3922652 | 0.7092059 | 0.2883031 | 1.1160714 | 0.5616836 | 0.2592593 |
| 6.1 | 0.6141575 | 0.7054054 | 0.6148649 | 0.8260870 | 0.3750000 | 0.3125481 | 0.3173846 | 0.9711378 | 0.9810355 | 2.349392 | 1.2147568 | 0.9583333 | 0.7517107 | 0.5198238 | 0.7365854 | 0.2469758 | 1.1190053 | 0.2654639 | 0.1604938 |
| 7.1 | 0.7641026 | 0.5242452 | 0.6591577 | 0.6000000 | 0.7368421 | 0.2985661 | 0.8040881 | 0.9847842 | 1.0683937 | 2.336629 | 1.0196235 | 0.7644593 | 0.7346570 | 0.4330709 | 1.1907478 | NA | NA | NA | NA |
| 8.1 | 0.3086253 | 0.5849057 | 0.4244635 | 0.4800000 | 0.6285714 | 0.3107289 | 0.4988095 | 0.9729330 | 1.1970276 | 2.046184 | 1.2483425 | 0.8205567 | 0.9322093 | 0.6106195 | 0.7074830 | 0.5376532 | 1.1081531 | 0.8280255 | 0.7777778 |
| 9.1 | 0.7061258 | 0.6245520 | 0.6665232 | 0.7500000 | 0.6000000 | 0.8392562 | 0.7816092 | 0.9858376 | 1.1139822 | 1.964433 | 1.2366031 | 1.4981454 | 0.6565325 | 0.6274194 | 1.1429440 | 0.1755889 | 0.9843137 | 0.1965318 | 0.1428571 |
| 10.1 | 0.7718023 | 0.4665012 | 0.6230263 | 0.4400000 | 0.8536585 | 0.4080294 | 0.4489583 | 0.9825059 | 1.0069524 | 3.665535 | 1.3353756 | 1.8146307 | 0.8441433 | 0.3564103 | 0.7020702 | 0.2857955 | 0.9182948 | 0.3548851 | 0.3623188 |
| 11.1 | 0.6143411 | 0.5955649 | 0.5832497 | 0.8181818 | 0.8214286 | 0.7029466 | 0.8826356 | 1.0131178 | 0.9996619 | 1.171353 | 1.1260306 | 1.1241696 | 0.9644747 | 0.9585122 | 0.6530850 | 0.3531202 | 1.1344828 | 0.3829787 | 0.3703704 |
| 12.1 | 0.7705287 | 0.7352587 | 0.6868100 | 0.6818182 | 0.4000000 | 0.7821395 | 0.8580247 | 1.0048024 | 1.0193132 | 1.220186 | 0.8205479 | 1.7769486 | 0.9071938 | 0.6742857 | 0.9170213 | 0.3651407 | 1.1017613 | 0.5875796 | 0.4262295 |
| 13.1 | 0.7205560 | 0.5748599 | 0.6379633 | 0.8000000 | 0.5945946 | 0.3933007 | 0.8412844 | 0.9843384 | 1.0147048 | 2.641426 | 1.0343169 | 1.4672783 | 0.7078189 | 0.3921569 | 0.6055556 | 0.1689189 | 0.7602041 | 0.1854396 | 0.1234568 |
| 14.1 | 0.5388646 | 0.6171096 | 0.5854576 | 0.5714286 | 0.3571429 | 0.3886307 | 0.6150000 | 0.9679176 | 1.1095468 | 2.769071 | 1.4512663 | 2.2868976 | 0.8902148 | 0.5228758 | 0.7039518 | 0.3601359 | 0.9320883 | 0.4835590 | 0.2592593 |
| 15.1 | 0.1673114 | 0.4004955 | 0.3745438 | 0.3368421 | 0.1851852 | 0.1458749 | 0.3050847 | 0.9623137 | 1.0466700 | 1.361111 | 1.3379447 | 1.3552941 | 0.6887417 | 0.9751553 | 0.5820745 | 0.2655039 | 0.6830428 | 0.1347150 | 0.0952381 |
| 16.1 | 0.2872517 | 0.1958406 | 0.2637694 | 0.2895652 | 0.1714286 | 0.4290074 | 0.8201635 | 0.9788952 | 1.0586022 | 2.142991 | 1.4204156 | 2.2103825 | 0.7299742 | 0.6483376 | 0.5970149 | NA | NA | NA | NA |
| 17.1 | 0.2446634 | 0.2021563 | 0.2612958 | 0.2800000 | 0.2894737 | 0.1725975 | 0.2884615 | 0.9644903 | 1.0599136 | 2.632761 | 1.5583658 | 3.1660542 | 0.7332268 | 0.6041667 | 0.6070252 | 0.6666667 | 0.9264706 | 0.3474178 | 0.5263158 |
| 18.1 | 0.1956664 | 0.1590909 | 0.1982964 | 0.3846154 | 0.1621622 | 0.1332756 | 0.3225000 | 0.9176678 | 1.0951118 | 1.540048 | 1.1216572 | 2.7861528 | 0.7603960 | 0.7338129 | 1.2998267 | 0.5008787 | 1.1421569 | 0.7168459 | 0.3703704 |
| 19.1 | 0.5484444 | 0.6816514 | 0.6327913 | 0.9411765 | 0.7142857 | 0.6726554 | 0.7635853 | 1.0186102 | 1.0286753 | 2.615610 | 1.2084392 | 2.6221228 | 0.6307008 | 0.4625850 | 0.8808889 | 0.7505774 | 1.0209059 | 0.1552198 | 0.1851852 |
| 20.1 | 0.1738693 | 0.4141759 | 0.6471132 | 0.5333333 | 0.3157895 | 0.7136095 | 0.8387097 | 1.0340028 | 1.0453706 | 3.529240 | 1.2608368 | 1.6038576 | 0.6960352 | 0.3685567 | 0.6601650 | 0.1377953 | 0.9054622 | 0.1533333 | 0.1230769 |
| 21.1 | 0.2930187 | 0.1722595 | 0.3778626 | 0.6250000 | 0.4500000 | 0.6796849 | 0.7768924 | 1.0045268 | 1.0491827 | 1.803975 | 1.2245062 | 1.4547284 | 0.5526565 | 0.6750000 | 1.6984925 | 0.2113254 | 0.9689781 | 0.2971014 | 0.2323232 |
| 22.1 | 0.6120603 | 0.3154907 | 0.4909931 | 0.5882353 | 0.8571429 | 0.7436083 | 0.6534979 | 0.9949697 | 1.1884034 | 1.687400 | 1.2295189 | 1.4203516 | 0.8420735 | 0.7324561 | 0.4223003 | 0.2919109 | 1.0617761 | 0.6226415 | 0.5217391 |
| 23.1 | 0.3644752 | 0.2955556 | 0.3299377 | 0.6666667 | 0.6400000 | 0.2726450 | 0.8100000 | 0.9623571 | 1.0422465 | 1.544219 | 1.5155393 | 1.1520648 | 0.7255521 | 0.9841270 | 0.4108527 | 0.1536174 | 1.0731707 | 0.4244373 | 0.4000000 |
| 24.1 | 0.5350501 | 0.3480114 | 0.4440535 | 0.5333333 | 0.8000000 | 0.6160173 | 0.4104348 | 0.9850878 | 1.1019697 | 2.048939 | 1.3736817 | 1.1574557 | 0.3929825 | 0.6721854 | 0.6623681 | 0.2011173 | 1.3056058 | 0.4013158 | 0.4347826 |
| 25.1 | 0.4878661 | 0.2348651 | 0.3546893 | 0.6666667 | 1.0000000 | 0.4188665 | 0.8152585 | 0.9636486 | 0.9610544 | 2.716135 | 1.8820856 | 2.0229358 | 0.8764309 | 0.6875000 | 0.6529412 | 0.3127753 | 1.3176692 | 0.3857868 | 0.4800000 |
#Membership function value
# 1-results for the traits inversely related to salt tolerance: which are electrolyte leakage, Na, K and Ca
MFV <- STI %>%
mutate(SL = (STI$Shoot_Length - min(STI$Shoot_Length))/(max(STI$Shoot_Length)- min(STI$Shoot_Length)),
RL = (STI$Root_Length - min(STI$Root_Length))/(max(STI$Root_Length)- min(STI$Root_Length)),
PH = (STI$Plant_Height - min(STI$Plant_Height))/(max(STI$Plant_Height)- min(STI$Plant_Height)),
NL = (STI$Number_Leaves - min(STI$Number_Leaves))/(max(STI$Number_Leaves)- min(STI$Number_Leaves)),
LA = (STI$Leaf_Area - min(STI$Leaf_Area))/(max(STI$Leaf_Area)- min(STI$Leaf_Area)),
FW = (STI$Fresh_Weight - min(STI$Fresh_Weight))/(max(STI$Fresh_Weight)- min(STI$Fresh_Weight)) ,
DW = (STI$Dry_Weight - min(STI$Dry_Weight))/(max(STI$Dry_Weight)- min(STI$Dry_Weight)),
RWC = (STI$Relative_water_content - min(STI$Relative_water_content))/(max(STI$Relative_water_content)- min(STI$Relative_water_content)),
EL = 1-(STI$Electrolyte_Leakage - min(STI$Electrolyte_Leakage))/(max(STI$Electrolyte_Leakage)- min(STI$Electrolyte_Leakage)),
CC = (STI$Chlorophyll_Content - min(STI$Chlorophyll_Content))/(max(STI$Chlorophyll_Content)- min(STI$Chlorophyll_Content)),
Na = 1-(STI$Na - min(STI$Na))/(max(STI$Na)- min(STI$Na)),
K = 1-(STI$K - min(STI$K))/(max(STI$K)- min(STI$K)),
Ca =1- (STI$Ca - min(STI$Ca))/(max(STI$Ca)- min(STI$Ca)),
Mg=(STI$Mg - min(STI$Mg))/(max(STI$Mg)- min(STI$Mg)),
K_Na= (STI$K_Na - min(STI$K_Na))/(max(STI$K_Na)- min(STI$K_Na)),
PR= (STI$Photsynthesis_Rate - min(STI$Photsynthesis_Rate, na.rm=T))/(max(STI$Photsynthesis_Rate, na.rm=T)- min(STI$Photsynthesis_Rate, na.rm=T)),
ICO2= (STI$Intercellular_CO2 - min(STI$Intercellular_CO2, na.rm=T))/(max(STI$Intercellular_CO2, na.rm=T)- min(STI$Intercellular_CO2, na.rm=T)),
TR= (STI$Transpiration_Rate - min(STI$Transpiration_Rate, na.rm=T))/(max(STI$Transpiration_Rate, na.rm=T)- min(STI$Transpiration_Rate, na.rm=T)),
SC= (STI$Stomatal_Conductance - min(STI$Stomatal_Conductance, na.rm=T))/(max(STI$Stomatal_Conductance, na.rm=T)- min(STI$Stomatal_Conductance, na.rm=T)))%>%
select(SL, RL, PH, NL, LA, FW, DW, RWC, EL, CC, Na, K, Ca, Mg, K_Na, PR, ICO2, TR, SC)
knitr::kable(MFV, caption = "Table 5.1. MFV table based on STI", align="c")| SL | RL | PH | NL | LA | FW | DW | RWC | EL | CC | Na | K | Ca | Mg | K_Na | PR | ICO2 | TR | SC |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0000000 | 0.8342641 | 1.0000000 | 0.5127944 | 0.6706373 | 0.5415308 | 0.9684647 | 0.7329858 | 1.0000000 | 0.3088107 | 0.0000000 | 0.5252233 | 0.9515557 | 0.9364151 | 0.0000000 | 0.4372282 | 0.2763024 | 0.3481463 | 0.3063813 |
| 0.5225294 | 0.8683744 | 0.5687487 | 0.5389841 | 0.4589041 | 0.3226203 | 0.7951708 | 1.0000000 | 0.8350309 | 0.2386207 | 0.2058062 | 0.3839946 | 0.9518709 | 0.7342830 | 0.1298617 | 0.8497332 | 0.5981036 | 1.0000000 | 1.0000000 |
| 0.0416954 | 0.0571408 | 0.1080738 | 0.0343740 | 0.0000000 | 0.0000000 | 0.0000000 | 0.6672545 | 0.2833031 | 0.2301033 | 0.6821910 | 0.3525687 | 0.8963558 | 0.9527996 | 0.5281751 | 0.0000000 | 0.3231526 | 0.0000000 | 0.0000000 |
| 0.5708628 | 0.9700860 | 0.8007147 | 0.8259322 | 0.3912671 | 0.5093267 | 0.6047008 | 0.2351993 | 0.6588033 | 0.4108210 | 0.4410979 | 0.4502459 | 0.8629350 | 0.6429858 | 0.2373242 | 0.2719969 | 0.4655524 | 0.2316787 | 0.2393509 |
| 0.3693288 | 0.7471738 | 0.4883085 | 0.2639922 | 0.8360315 | 0.8978110 | 0.9292329 | 0.3241139 | 0.0000000 | 0.2317055 | 0.5457070 | 0.7557030 | 1.0000000 | 0.4936569 | 0.1564965 | 0.3118398 | 0.6823363 | 0.3882249 | 0.2017880 |
| 0.6172555 | 0.9481864 | 0.7679469 | 0.8259322 | 0.3236301 | 0.2856111 | 0.1112167 | 0.1470450 | 0.9126550 | 0.2529688 | 0.6636635 | 0.6286436 | 0.9168199 | 0.6277044 | 0.3382892 | 0.2503183 | 0.6869594 | 0.1201028 | 0.0953597 |
| 0.8243836 | 0.6337638 | 0.8496011 | 0.4839858 | 0.7152127 | 0.2666469 | 0.8764943 | 0.1845734 | 0.6804469 | 0.6056780 | 0.6673075 | 0.8124649 | 0.9973317 | 0.5978639 | 0.2146515 | NA | NA | NA | NA |
| 0.1952055 | 0.7390464 | 0.4169407 | 0.3024911 | 0.5980431 | 0.2831436 | 0.3964837 | 0.1519818 | 0.3385234 | 0.2303674 | 0.7502312 | 0.5970048 | 0.9740356 | 0.9435420 | 0.4676884 | 0.6830325 | 0.6698591 | 0.6293032 | 0.7605364 |
| 0.7442969 | 0.8078568 | 0.8631795 | 0.7108541 | 0.5671233 | 1.0000000 | 0.8411492 | 0.1874700 | 0.5592676 | 0.5685529 | 0.7735715 | 0.6080637 | 0.6926474 | 0.4611611 | 0.4916311 | 0.1440488 | 0.4747217 | 0.0577091 | 0.0763547 |
| 0.8350198 | 0.5335430 | 0.7829926 | 0.2419929 | 0.8416305 | 0.4151151 | 0.3180992 | 0.1783078 | 0.8437650 | 0.2261638 | 0.2878975 | 0.5150171 | 0.5612177 | 0.7894435 | 0.1053972 | 0.3081068 | 0.3706938 | 0.2010420 | 0.3128436 |
| 0.6175091 | 0.7575467 | 0.7096642 | 0.8139761 | 0.8067515 | 0.8151195 | 1.0000000 | 0.2624920 | 0.8631439 | 0.1881211 | 1.0000000 | 0.7122262 | 0.8479516 | 1.0000000 | 0.9634945 | 0.4083292 | 0.7113476 | 0.2264708 | 0.3215198 |
| 0.8332604 | 1.0000000 | 0.9005783 | 0.6077321 | 0.3506849 | 0.9225310 | 0.9613026 | 0.2396242 | 0.8109084 | 0.3930980 | 0.9860579 | 1.0000000 | 0.5768663 | 0.8997697 | 0.5584236 | 0.4262233 | 0.6597874 | 0.4116645 | 0.3817128 |
| 0.7642301 | 0.7216109 | 0.8105291 | 0.7864769 | 0.5612736 | 0.3951381 | 0.9349807 | 0.1833474 | 0.8231580 | 0.1512091 | 0.5802862 | 0.7986233 | 0.7054658 | 0.5509025 | 0.1563421 | 0.1341196 | 0.1215854 | 0.0476690 | 0.0554491 |
| 0.5132489 | 0.7949398 | 0.7137344 | 0.4407728 | 0.3043053 | 0.3888040 | 0.5791780 | 0.1381892 | 0.5710574 | 0.2276250 | 0.5438428 | 0.4058446 | 0.3650952 | 0.8700597 | 0.3426388 | 0.4187730 | 0.3924285 | 0.3175107 | 0.2017880 |
| 0.0000000 | 0.4189831 | 0.3249133 | 0.0859712 | 0.1182141 | 0.0595475 | 0.0918767 | 0.1227783 | 0.7381909 | 0.1329734 | 0.9458231 | 0.5125969 | 0.7519705 | 0.5175211 | 0.9872138 | 0.2778999 | 0.0000000 | 0.0017559 | 0.0250411 |
| 0.1656807 | 0.0637829 | 0.1206998 | 0.0144670 | 0.1033268 | 0.4435681 | 0.9017708 | 0.1683783 | 0.7064738 | 0.1445763 | 0.7225921 | 0.4349068 | 0.3968703 | 0.5896698 | 0.5214432 | NA | NA | NA | NA |
| 0.1068509 | 0.0747446 | 0.1161398 | 0.0000000 | 0.2310743 | 0.0957922 | 0.0657389 | 0.1287642 | 0.7029881 | 0.1523505 | 0.5827600 | 0.3049537 | 0.0000000 | 0.5953613 | 0.4584921 | 0.8750871 | 0.3835766 | 0.1942830 | 0.4895644 |
| 0.0391685 | 0.0000000 | 0.0000000 | 0.1582261 | 0.0932988 | 0.0424588 | 0.1192599 | 0.0000000 | 0.6094273 | 0.6903903 | 0.8947355 | 0.7163462 | 0.1577650 | 0.6429022 | 0.6432600 | 0.6282885 | 0.7234399 | 0.5286694 | 0.3215198 |
| 0.5264821 | 0.9069588 | 0.8009945 | 1.0000000 | 0.6908023 | 0.7740346 | 0.8128090 | 0.2775962 | 0.7860230 | 0.3650370 | 0.5876568 | 0.6345949 | 0.2258833 | 0.4159607 | 0.2567142 | 1.0000000 | 0.5323812 | 0.0203157 | 0.1219668 |
| 0.0090589 | 0.4427269 | 0.8273969 | 0.3831554 | 0.2595530 | 0.8295818 | 0.9309323 | 0.3199268 | 0.7416450 | 0.1936196 | 0.3268105 | 0.5852348 | 0.6487473 | 0.5302833 | 0.1227079 | 0.0877877 | 0.3504730 | 0.0186082 | 0.0550398 |
| 0.1736470 | 0.0228555 | 0.3310315 | 0.5217972 | 0.4047945 | 0.7835690 | 0.8337327 | 0.2388663 | 0.7315120 | 1.0000000 | 0.8193829 | 0.6194593 | 0.7106775 | 0.2793985 | 0.5594416 | 0.1972477 | 0.4505569 | 0.1487393 | 0.1727621 |
| 0.6143585 | 0.2714483 | 0.5395884 | 0.4661922 | 0.8454012 | 0.8702701 | 0.6397110 | 0.2125838 | 0.3614476 | 0.0088903 | 0.8526658 | 0.6147372 | 0.7249535 | 0.7858219 | 0.6413263 | 0.3172105 | 0.5967815 | 0.4434007 | 0.4846327 |
| 0.2723542 | 0.2368488 | 0.2426817 | 0.5848161 | 0.6104110 | 0.2314894 | 0.8857901 | 0.1228976 | 0.7499491 | 0.0000000 | 0.8935448 | 0.3452975 | 0.8363673 | 0.5819320 | 1.0000000 | 0.1113413 | 0.6147364 | 0.2639969 | 0.3534483 |
| 0.5079796 | 0.3278914 | 0.4530549 | 0.3831554 | 0.7835616 | 0.6972148 | 0.2575259 | 0.1854081 | 0.5911980 | 0.1953306 | 0.7494444 | 0.4789316 | 0.8341286 | 0.0000000 | 0.5554304 | 0.1820515 | 0.9809914 | 0.2430686 | 0.3909295 |
| 0.4428015 | 0.1315140 | 0.2883114 | 0.5848161 | 1.0000000 | 0.4298137 | 0.8940584 | 0.1264494 | 0.9657671 | 0.1880095 | 0.5589565 | 0.0000000 | 0.4747130 | 0.8459405 | 0.5772562 | 0.3482701 | 1.0000000 | 0.2290126 | 0.4396552 |
####------PCA on MFV-------###
#doing the PCA, adding the scaling into the function
MFV_salt.pca <- prcomp((na.omit(MFV[-c(8, 17, 20)])), scale. = TRUE)
#results of the PCA
MFV_salt.pca$sdev## [1] 2.1959249 1.6698449 1.3565852 1.2856275 1.2158618 1.1326328 0.9173147
## [8] 0.8378035 0.7115879 0.6292612 0.4811136 0.4495871 0.3451879 0.2533067
## [15] 0.2128659 0.1474844 0.0741877
summary(MFV_salt.pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.1959 1.6698 1.3566 1.28563 1.21586 1.13263 0.9173
## Proportion of Variance 0.2837 0.1640 0.1082 0.09723 0.08696 0.07546 0.0495
## Cumulative Proportion 0.2837 0.4477 0.5559 0.65315 0.74011 0.81558 0.8651
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 0.83780 0.71159 0.62926 0.48111 0.44959 0.34519 0.25331
## Proportion of Variance 0.04129 0.02979 0.02329 0.01362 0.01189 0.00701 0.00377
## Cumulative Proportion 0.90636 0.93615 0.95944 0.97306 0.98495 0.99196 0.99573
## PC15 PC16 PC17
## Standard deviation 0.21287 0.14748 0.07419
## Proportion of Variance 0.00267 0.00128 0.00032
## Cumulative Proportion 0.99840 0.99968 1.00000
#check, if the sums equal to quantity of observations (=17)
sum((MFV_salt.pca$sdev)^2)## [1] 17
#plotting the results in order to evaluate the principal components
MFV_plot <- screeplot(MFV_salt.pca,
npcs = length(MFV_salt.pca$sdev),
type = "lines")#plotting the results of the PCA
#making a scatterplot of PC1 and PC2
#creating data.frame for data labels in future plot
MFV_labels <- data.frame(Acc_loc$Elevation[-c(7, 16)])
row.names(MFV_labels) <- paste0("Ess-", c(1:6, 8:15, 17:25))
#creating the plot
MFV_pc12 <- autoplot(MFV_salt.pca, loadings = TRUE, loadings.colour = 'blue',
loadings.label = TRUE, loadings.label.size = 5, #plotting the variables
data = MFV_labels, shape = FALSE, label.size = 3, #plotting the accessions
frame = TRUE) #plotting the frame (to see if there are any groups)
MFV_pc12 + ggtitle("Scatterplot of PC1 and PC2") #adding the title#putting absolute values of the loading scores from PCA1 into a vector
MFV_loading_scores <- abs(MFV_salt.pca$rotation[,1])
#ranking the variables
MFV_var_scores_ranked <- sort(MFV_loading_scores, decreasing = TRUE)
# We used 8 highest ranked variables to include in the final MFV-based ranking
# of salt tolerance accessions
print(MFV_var_scores_ranked) # Result from PCA analysis (from PCA_analysis.R)## PH SL RL NL DW FW
## 0.4179220239 0.3843371322 0.3655251106 0.3440154296 0.3304446450 0.3118996483
## LA K_Na Na K Ca EL
## 0.2467265352 0.2306101451 0.1746912100 0.1736000724 0.1438780842 0.1395916180
## Mg TR SC PR CC
## 0.0455479807 0.0433627723 0.0156462600 0.0100021446 0.0005731188
# We used 6 highest ranked variables to include in the final MFV based ranking of salt tolerance
# Add into the data the mean MFV column from the 6 important variables
MFV <- MFV %>%
mutate(Mean = rowMeans(MFV[c(1:4, 6:7)], na.rm=T))%>%
select(SL, RL, PH, NL, LA, FW, DW, RWC, EL, CC, Na, K, Ca, Mg, K_Na, PR, ICO2, TR, SC, Mean)
# create the ranking table out of MFV mean from 6 important variables
MFV_Ranked <- MFV[order(-MFV$Mean),]
MFV_Ranked <- MFV_Ranked[20]
MFV_Ranked$Category <- c("Highly tolerant",(rep("Tolerant", 4)), (rep("Moderately tolerant", 15)), (rep("Sensitive", 4)), "Highly sensitive" )
knitr::kable(MFV_Ranked, caption = "Table 7.1. Salt tolerance ranking based on MFV mean from 6 variables identified by PCA", align="c")| Mean | Category | |
|---|---|---|
| 12 | 0.8709007 | Highly tolerant |
| 9 | 0.8278894 | Tolerant |
| 1 | 0.8095090 | Tolerant |
| 19 | 0.8035465 | Tolerant |
| 11 | 0.7856359 | Tolerant |
| 13 | 0.7354943 | Moderately tolerant |
| 4 | 0.7136039 | Moderately tolerant |
| 7 | 0.6558126 | Moderately tolerant |
| 5 | 0.6159745 | Moderately tolerant |
| 2 | 0.6027379 | Moderately tolerant |
| 6 | 0.5926915 | Moderately tolerant |
| 14 | 0.5717796 | Moderately tolerant |
| 20 | 0.5704754 | Moderately tolerant |
| 22 | 0.5669281 | Moderately tolerant |
| 10 | 0.5211271 | Moderately tolerant |
| 25 | 0.4618859 | Moderately tolerant |
| 21 | 0.4444388 | Moderately tolerant |
| 24 | 0.4378037 | Moderately tolerant |
| 23 | 0.4089967 | Moderately tolerant |
| 8 | 0.3888851 | Moderately tolerant |
| 16 | 0.2849949 | Sensitive |
| 15 | 0.1635486 | Sensitive |
| 17 | 0.0765444 | Sensitive |
| 18 | 0.0598522 | Sensitive |
| 3 | 0.0402140 | Highly sensitive |
#table with MFV from 6 important variables of PCA
important_var <- na.omit(MFV)
important_var <- MFV[c(1:4, 6:7)]
#make the dendogram
dend <- important_var %>%
scale %>%
dist(method = "euclidean") %>%
hclust(method = "ward.D2") %>%
as.dendrogram %>%
set("branches_k_color",
value = c("#CC66FF", "#33CCFF","#99FF66", "#00CC33"),
k = 4)%>% # make the 4 groups and color for groups
plot(main = "MFV Dendrogram") # plot dendogramFigure 8.1. The MFV dendogram based on 8 variables identified by PCA
# Correlation between mean MFV (of 8 important ) and elevation
Elev_Cor <- as.data.frame(MFV$Mean)
Elev_Cor$Elevation <- Acc_loc$Elevation
cor_1 <- rcorr(as.matrix(Elev_Cor))
cor_1_result <- paste(round(cor_1$r[2,1], 2), sep = " - correlation coefficient")
knitr::kable(cor_1_result, caption = "Table 8.1. Correlation of mean MFV and elevation", align="c")| x |
|---|
| -0.07 |
(From your objectives) The priliminary results, despite being slightly different from that in the article produced the ranking that corresponds to the groups (Highly tolerant - Highly sensitive) outlined in the article.
The analysis is not yet completed, so conclusions will be drawn later.